Literature DB >> 28931315

The Liver Toxicity Knowledge Base (LKTB) and drug-induced liver injury (DILI) classification for assessment of human liver injury.

Shraddha Thakkar1, Minjun Chen1, Hong Fang2, Zhichao Liu1, Ruth Roberts3,4, Weida Tong1.   

Abstract

INTRODUCTION: Drug-induced liver injury (DILI) is challenging for drug development, clinical practice and regulation. The Liver Toxicity Knowledge Base (LTKB) provides essential data for DILI study. Areas covered: The LTKB provided various types of data that can be used to assess and predict DILI. Among much information available, several reference drug lists with annotated human DILI risk are of important. The LTKB DILI classification data include DILI severity concern determined by the FDA drug labeling, DILI severity score from the NIH LiverTox database, and other DILI classification schemes from the literature. Overall, ~1000 drugs were annotated with at least one classification scheme, of which around 750 drugs were flagged for some degree of DILI risk. Expert commentary: The LTKB provides a centralized repository of information for DILI study and predictive model development. The DILI classification data in LTKB could be a useful resource for developing biomarkers, predictive models and assessing data from emerging technologies such as in silico, high-throughput and high-content screening methodologies. In coming years, streamlining the prediction process by including DILI predictive models for both DILI severity and types in LTKB would enhance the identification of compounds with the DILI potential earlier in drug development and risk assessment.

Entities:  

Keywords:  DILI; Drug-Induced Liver Injury; LTKB; Liver Toxicity Knowledgebase; computational toxicology; drug classification for DILI; human liver injury

Mesh:

Substances:

Year:  2017        PMID: 28931315     DOI: 10.1080/17474124.2018.1383154

Source DB:  PubMed          Journal:  Expert Rev Gastroenterol Hepatol        ISSN: 1747-4124            Impact factor:   3.869


  13 in total

Review 1.  Strategies for Early Prediction and Timely Recognition of Drug-Induced Liver Injury: The Case of Cyclin-Dependent Kinase 4/6 Inhibitors.

Authors:  Emanuel Raschi; Fabrizio De Ponti
Journal:  Front Pharmacol       Date:  2019-10-24       Impact factor: 5.810

Review 2.  Managing the challenge of drug-induced liver injury: a roadmap for the development and deployment of preclinical predictive models.

Authors:  Richard J Weaver; Eric A Blomme; Amy E Chadwick; Ian M Copple; Helga H J Gerets; Christopher E Goldring; Andre Guillouzo; Philip G Hewitt; Magnus Ingelman-Sundberg; Klaus Gjervig Jensen; Satu Juhila; Ursula Klingmüller; Gilles Labbe; Michael J Liguori; Cerys A Lovatt; Paul Morgan; Dean J Naisbitt; Raymond H H Pieters; Jan Snoeys; Bob van de Water; Dominic P Williams; B Kevin Park
Journal:  Nat Rev Drug Discov       Date:  2019-11-20       Impact factor: 84.694

Review 3.  Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction.

Authors:  Antonio Segovia-Zafra; Daniel E Di Zeo-Sánchez; Carlos López-Gómez; Zeus Pérez-Valdés; Eduardo García-Fuentes; Raúl J Andrade; M Isabel Lucena; Marina Villanueva-Paz
Journal:  Acta Pharm Sin B       Date:  2021-11-18       Impact factor: 11.413

4.  CYP2C9 and 3A4 play opposing roles in bioactivation and detoxification of diphenylamine NSAIDs.

Authors:  Mary Alexandra Schleiff; Samantha Crosby; Madison Blue; Benjamin Mark Schleiff; Gunnar Boysen; Grover Paul Miller
Journal:  Biochem Pharmacol       Date:  2021-11-05       Impact factor: 5.858

5.  NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism.

Authors:  Anush Chiappino-Pepe; Kiandokht Haddadi; Homa MohammadiPeyhani; Jasmin Hafner; Noushin Hadadi; Vassily Hatzimanikatis
Journal:  Elife       Date:  2021-08-03       Impact factor: 8.140

6.  Impacts of diphenylamine NSAID halogenation on bioactivation risks.

Authors:  Mary Alexandra Schleiff; Sasin Payakachat; Benjamin Mark Schleiff; S Joshua Swamidass; Gunnar Boysen; Grover Paul Miller
Journal:  Toxicology       Date:  2021-06-06       Impact factor: 4.571

Review 7.  Can Bile Salt Export Pump Inhibition Testing in Drug Discovery and Development Reduce Liver Injury Risk? An International Transporter Consortium Perspective.

Authors:  J Gerry Kenna; Kunal S Taskar; Christina Battista; David L Bourdet; Kim L R Brouwer; Kenneth R Brouwer; David Dai; Christoph Funk; Michael J Hafey; Yurong Lai; Jonathan Maher; Y Anne Pak; Jenny M Pedersen; Joseph W Polli; A David Rodrigues; Paul B Watkins; Kyunghee Yang; Robert W Yucha
Journal:  Clin Pharmacol Ther       Date:  2018-11       Impact factor: 6.875

8.  Deep Learning on High-Throughput Transcriptomics to Predict Drug-Induced Liver Injury.

Authors:  Ting Li; Weida Tong; Ruth Roberts; Zhichao Liu; Shraddha Thakkar
Journal:  Front Bioeng Biotechnol       Date:  2020-11-27

9.  ProTox-II: a webserver for the prediction of toxicity of chemicals.

Authors:  Priyanka Banerjee; Andreas O Eckert; Anna K Schrey; Robert Preissner
Journal:  Nucleic Acids Res       Date:  2018-07-02       Impact factor: 16.971

10.  Prediction Is a Balancing Act: Importance of Sampling Methods to Balance Sensitivity and Specificity of Predictive Models Based on Imbalanced Chemical Data Sets.

Authors:  Priyanka Banerjee; Frederic O Dehnbostel; Robert Preissner
Journal:  Front Chem       Date:  2018-08-28       Impact factor: 5.221

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.